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Commentary on ‘Current computational models do not reveal the importance of the nervous system in long‐term control of arterial pressure’

2009· letter· en· W2159777697 on OpenAlexaff
Jean‐Pierre Montani, Bruce N. Van Vliet

Bibliographic record

VenueExperimental Physiology · 2009
Typeletter
Languageen
FieldMedicine
TopicNitric Oxide and Endothelin Effects
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsNatriuresisBlood pressureAutonomic nervous systemSympathetic nervous systemNeuroscienceHormoneKidneyMedicineInternal medicinePsychologyHeart rate

Abstract

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In their review, Osborn et al. (2009) put forward many arguments in favour of an important role of the nervous system in long-term control of blood pressure (BP). However, the issue is not whether dysregulation of the autonomic nervous system can or cannot affect BP, but by which mechanisms such a dysregulation may affect BP. As pointed out in our review (Montani & Van Vliet, 2009), there is considerable evidence for the role of the kidney and the pressure–natriuresis relationship (PNR) in long-term BP control. This viewpoint is challenged by Osborn et al. (2009), who argue: (1) that pressure natriuresis is important only under pathophysiological situations, as a ‘back-up’ to hormonal controllers of sodium balance; (2) that the kidney adapts to BP changes, and that sodium balance is controlled by hormonal systems independently of BP; (3) that Guyton's model regulates BP via the control of blood volume, which is not well related to BP; (4) that Guyton's model minimizes the role of the central nervous system (CNS) in long-term BP control; and (5) that an alternative mechanism of long-term BP control can be proposed with the sympathetic nervous system acting on blood volume distribution rather than via the kidney. The authors conclude that Guyton's model cannot serve as a starting point and that new mathematical models should be developed based on different core concepts. We will comment briefly, point by point. First, the acute PNR has been demonstrated in diverse situations, including isolated perfused kidneys, anaesthetized preparations and conscious animals. The relationship is continuous and operates over a wide range of physiological pressures without an apparent threshold. However, it is clear that other systems act to modify this relationship, altering the kidney's ability to excrete salt and water at any given level of BP and thereby permitting salt balance to adjust even in the absence of a change in BP. Second, there is strong experimental support for a sustained effect of BP per se on sodium excretion. In dogs instrumented for separate control of the perfusion pressure to each kidney, the acute PNR did not adapt during long-term (12 day) changes in arterial pressure (Mizelle et al. 1993). Third, the authors misinterpret Guyton's reliance on blood volume in regulating BP. Blood volume is an intermediary used to alter BP until sodium balance is again achieved. More importantly, BP is not a function of blood volume per se but of the ‘volume in excess’ in the vascular tree. Substances such as the vasoconstrictors angiotensin and noradrenaline, which decrease vascular capacitance at the same time as they promote sodium retention, thereby lead to hypertension in a volume-contracted state. Fourth, Guyton's analysis does not argue against the potential for the CNS to influence long-term BP control, it simply requires that the CNS modify the PNR so that salt balance can be achieved at the new BP level. Although the renal nerves are one pathway by which the CNS may modify the PNR, there are many other possibilities. While improving what the authors call ‘the most widely accepted model for long-term control of arterial pressure’ is an ambitious task, there is no doubt that modelling the regulation of long-term BP is worthy of much further study. However, to be useful, mathematical models must be well rooted in empirical data to confirm the behaviour of the complete model and its components. With this in mind, it is our belief that mathematical models of long-term BP control should incorporate a pressure–natriuresis mechanism that does not adapt to pressure itself, but is sensitive to modulation by neurohumoral systems.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.717
Threshold uncertainty score0.727

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.279
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2009
Admission routes1
Has abstractyes

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